This dataset looks at all types of hate crimes in New York counties by the type of hate crime from 2010 to 2016.
My Caveat:
Flawed hate crime data collection - we should know how the data was collected
(Nathan Yau of Flowing Data, Dec 5, 2017)
Data can provvictim_cate you with important information, but when the collection process is flawed, there’s not much you can do. Ken Schwencke, reporting for ProPublica, researched the tiered system that the FBI relies on to gather hate crime data for the United States:
“Under a federal law passed in 1990, the FBI is required to track and tabulate crimes in which there was ‘manifest evvictim_catence of prejudice’ against a host of protected groups, regardless of differences in how state laws define who’s protected. The FBI, in turn, relies on local law enforcement agencies to collect and submit this data, but can’t compel them to do so.”
This is a link to the ProPublica Article: https://www.propublica.org/article/why-america-fails-at-gathering-hate-crime-statistics
Here is a data visualization of where hate crimes do NOT get reported around the country (Ken Schwencke, 2017): https://projects.propublica.org/graphics/hatecrime-map
So now we know that there is possible bias in the dataset, what can we do with it?
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Rows: 423 Columns: 44
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): County, Crime Type
dbl (42): Year, Anti-Male, Anti-Female, Anti-Transgender, Anti-Gender Identi...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
After cleaning up the variable names, look at the structure of the data. Since there are 44 variables in this dataset, you can use “summary” to decide which hate crimes to focus on. In the output of “summary”, look at the min/max values. Some have a max-vale of 1.
county year crime_type anti-male
Length:423 Min. :2010 Length:423 Min. :0.000000
Class :character 1st Qu.:2011 Class :character 1st Qu.:0.000000
Mode :character Median :2013 Mode :character Median :0.000000
Mean :2013 Mean :0.007092
3rd Qu.:2015 3rd Qu.:0.000000
Max. :2016 Max. :1.000000
anti-female anti-transgender anti-gender_identity_expression
Min. :0.00000 Min. :0.00000 Min. :0.00000
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
Median :0.00000 Median :0.00000 Median :0.00000
Mean :0.01655 Mean :0.04728 Mean :0.05674
3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
Max. :1.00000 Max. :5.00000 Max. :3.00000
anti-age* anti-white anti-black
Min. :0.00000 Min. : 0.0000 Min. : 0.000
1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.: 0.000
Median :0.00000 Median : 0.0000 Median : 1.000
Mean :0.05201 Mean : 0.3357 Mean : 1.761
3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.: 2.000
Max. :9.00000 Max. :11.0000 Max. :18.000
anti-american_indian/alaskan_native anti-asian
Min. :0.000000 Min. :0.0000
1st Qu.:0.000000 1st Qu.:0.0000
Median :0.000000 Median :0.0000
Mean :0.007092 Mean :0.1773
3rd Qu.:0.000000 3rd Qu.:0.0000
Max. :1.000000 Max. :8.0000
anti-native_hawaiian/pacific_islander anti-multi-racial_groups anti-other_race
Min. :0 Min. :0.00000 Min. :0
1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
Median :0 Median :0.00000 Median :0
Mean :0 Mean :0.08511 Mean :0
3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
Max. :0 Max. :3.00000 Max. :0
anti-jewish anti-catholic anti-protestant anti-islamic_(muslim)
Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 3.981 Mean : 0.2695 Mean :0.02364 Mean : 0.4704
3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :82.000 Max. :12.0000 Max. :1.00000 Max. :10.0000
anti-multi-religious_groups anti-atheism/agnosticism
Min. : 0.00000 Min. :0
1st Qu.: 0.00000 1st Qu.:0
Median : 0.00000 Median :0
Mean : 0.07565 Mean :0
3rd Qu.: 0.00000 3rd Qu.:0
Max. :10.00000 Max. :0
anti-religious_practice_generally anti-other_religion anti-buddhist
Min. :0.000000 Min. :0.000 Min. :0
1st Qu.:0.000000 1st Qu.:0.000 1st Qu.:0
Median :0.000000 Median :0.000 Median :0
Mean :0.007092 Mean :0.104 Mean :0
3rd Qu.:0.000000 3rd Qu.:0.000 3rd Qu.:0
Max. :2.000000 Max. :4.000 Max. :0
anti-eastern_orthodox_(greek,_russian,_etc.) anti-hindu
Min. :0.000000 Min. :0.000000
1st Qu.:0.000000 1st Qu.:0.000000
Median :0.000000 Median :0.000000
Mean :0.002364 Mean :0.002364
3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :1.000000 Max. :1.000000
anti-jehovahs_witness anti-mormon anti-other_christian anti-sikh
Min. :0 Min. :0 Min. :0.00000 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0.00000 1st Qu.:0
Median :0 Median :0 Median :0.00000 Median :0
Mean :0 Mean :0 Mean :0.01655 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:0
Max. :0 Max. :0 Max. :3.00000 Max. :0
anti-hispanic anti-arab anti-other_ethnicity/national_origin
Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 0.3735 Mean :0.06619 Mean : 0.2837
3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :17.0000 Max. :2.00000 Max. :19.0000
anti-non-hispanic* anti-gay_male anti-gay_female
Min. :0 Min. : 0.000 Min. :0.0000
1st Qu.:0 1st Qu.: 0.000 1st Qu.:0.0000
Median :0 Median : 0.000 Median :0.0000
Mean :0 Mean : 1.499 Mean :0.2411
3rd Qu.:0 3rd Qu.: 1.000 3rd Qu.:0.0000
Max. :0 Max. :36.000 Max. :8.0000
anti-gay_(male_and_female) anti-heterosexual anti-bisexual
Min. :0.0000 Min. :0.000000 Min. :0.000000
1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.000000
Median :0.0000 Median :0.000000 Median :0.000000
Mean :0.1017 Mean :0.002364 Mean :0.004728
3rd Qu.:0.0000 3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :4.0000 Max. :1.000000 Max. :1.000000
anti-physical_disability anti-mental_disability total_incidents
Min. :0.00000 Min. :0.000000 Min. : 1.00
1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.: 1.00
Median :0.00000 Median :0.000000 Median : 3.00
Mean :0.01182 Mean :0.009456 Mean : 10.09
3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.: 10.00
Max. :1.00000 Max. :1.000000 Max. :101.00
total_victims total_offenders
Min. : 1.00 Min. : 1.00
1st Qu.: 1.00 1st Qu.: 1.00
Median : 3.00 Median : 3.00
Mean : 10.48 Mean : 11.77
3rd Qu.: 10.00 3rd Qu.: 11.00
Max. :106.00 Max. :113.00
I decided I would only look at the hate-crime types with a max number of 9 or more. That way I can focus on the most prominent types of hate-crimes.
Check the dimensions and the summary to make sure no missing values
Also check the dimensions to count how many variables remain
dim(hatecrimes2)
[1] 423 12
# There are currently 12 variables with 423 rows.summary(hatecrimes2)
county year anti-black anti-white
Length:423 Min. :2010 Min. : 0.000 Min. : 0.0000
Class :character 1st Qu.:2011 1st Qu.: 0.000 1st Qu.: 0.0000
Mode :character Median :2013 Median : 1.000 Median : 0.0000
Mean :2013 Mean : 1.761 Mean : 0.3357
3rd Qu.:2015 3rd Qu.: 2.000 3rd Qu.: 0.0000
Max. :2016 Max. :18.000 Max. :11.0000
anti-jewish anti-catholic anti-age* anti-islamic_(muslim)
Min. : 0.000 Min. : 0.0000 Min. :0.00000 Min. : 0.0000
1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.00000 1st Qu.: 0.0000
Median : 0.000 Median : 0.0000 Median :0.00000 Median : 0.0000
Mean : 3.981 Mean : 0.2695 Mean :0.05201 Mean : 0.4704
3rd Qu.: 3.000 3rd Qu.: 0.0000 3rd Qu.:0.00000 3rd Qu.: 0.0000
Max. :82.000 Max. :12.0000 Max. :9.00000 Max. :10.0000
anti-multi-religious_groups anti-gay_male anti-hispanic
Min. : 0.00000 Min. : 0.000 Min. : 0.0000
1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.: 0.0000
Median : 0.00000 Median : 0.000 Median : 0.0000
Mean : 0.07565 Mean : 1.499 Mean : 0.3735
3rd Qu.: 0.00000 3rd Qu.: 1.000 3rd Qu.: 0.0000
Max. :10.00000 Max. :36.000 Max. :17.0000
anti-other_ethnicity/national_origin
Min. : 0.0000
1st Qu.: 0.0000
Median : 0.0000
Mean : 0.2837
3rd Qu.: 0.0000
Max. :19.0000
Convert from wide to long format
Look at each set of hate-crimes for each type for each year. Convert the dataset from wide to long with the pivot_longer function. It will take each column’s hate-crime type combine them all into one column called “victim_cat”. Then each cell count will go into the new column, “crimecount”.
Finally, we are only doing this for the quantitiative variables, which are in columns 3 - 10. Note the command facet_wrap requires (~) before “victim_cat”.
Look deeper into crimes against blacks, gay males, and jews
From the facet_wrap plot above, anti-black, anti-gay males, and anti-jewish categories seem to have highest rates of offenses reported. Filter out just for those 3 crimes.
Use the following commands to finalize your barplot: - position = “dodge” makes side-by-side bars, rather than stacked bars - stat = “identity” allows you to plot each set of bars for each year between 2010 and 2016 - ggtitle gives the plot a title - labs gives a title to the legend
plot2 <- hatenew |>ggplot()+geom_bar(aes(x=year, y=crimecount, fill = victim_cat),position ="dodge", stat ="identity") +labs(fill ="Hate Crime Type",y ="NUmber of Hate Crime Indicdents",title ="Hate Crime Type in NY Counties Between 2010-2016",caption ="Source: NY State Division of Criminal Justice Services")plot2
We can see that hate crimes against jews spiked in 2012. All other years were relatively consistent with a slight upward trend. There was also an upward trend in hate crimes against gay males. Finally, there appears to be a downward trend in hate crimes against blacks during this period.
What about the counties?
I have not dealt with the counties, but I think that is the next place to explore. I can make bar graphs by county instead of by year.
plot3 <- hatenew |>ggplot()+geom_bar(aes(x=county, y=crimecount, fill = victim_cat),position ="dodge", stat ="identity") +labs(fill ="Hate Crime Types",y ="Number of Hate Crime Incidents",title ="Hate Crime Type in NY Counties Between 2010-2016",caption ="source: NY State Division of Criminal Justice Services")plot3
So many counties
There are too many counties for this plot to make sense, but maybe we can just look at the 5 counties with the highest number of incidents. - use “group_by” to group each row by counties - use summarize to get the total sum of incidents by county - use arrange(desc) to arrange those sums of total incidents by counties in descending order.
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.
counties
# A tibble: 277 × 3
# Groups: year [7]
year county sum
<dbl> <chr> <dbl>
1 2012 Kings 136
2 2010 Kings 110
3 2016 Kings 101
4 2013 Kings 96
5 2014 Kings 94
6 2015 Kings 90
7 2011 Kings 86
8 2016 New York 86
9 2012 Suffolk 83
10 2013 New York 75
# ℹ 267 more rows
Top 5
To list the 5 counties with the highest total incidents, change group_by to: group_by(county), then use slice_max(order_by = sum, n=5) to list the 5 counties with highest total incidents
# A tibble: 5 × 2
county sum
<chr> <dbl>
1 Kings 713
2 New York 459
3 Suffolk 360
4 Nassau 298
5 Queens 235
Finally, create the barplot above, but only for the 5 counties in 2012 with the highest incidents of hate-crimes. The command “labs” is nice, because you can get a title, subtitle, y-axis label, and legend title, all in one command.
plot4 <- hatenew |>filter(county %in%c("Kings", "New York", "Suffolk", "Nassau", "Queens")) |>ggplot() +geom_bar(aes(x=county, y=crimecount, fill = victim_cat),position ="dodge", stat ="identity") +labs(y ="Number of Hate Crime Incidents",title ="5 Counties in NY with Highest Incidents of Hate Crimes",subtitle ="Between 2010-2016", fill ="Hate Crime Type",caption ="Source: NY State Division of Criminal Justice Services")plot4
How would calculations be affected by looking at hate crimes in counties per year by population densities?
Bring in census data for populations of New York counties. These are estimates from the 2010 census.
Rows: 62 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Geography
dbl (7): 2010, 2011, 2012, 2013, 2014, 2015, 2016
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Clean the county name to match the other dataset
Rename the variable “Geography” as “county” so that it matches in the other dataset.
# A tibble: 6 × 3
county year population
<chr> <dbl> <dbl>
1 Albany , New York 2010 304078
2 Allegany , New York 2010 48949
3 Bronx , New York 2010 1388240
4 Broome , New York 2010 200469
5 Cattaraugus , New York 2010 80249
6 Cayuga , New York 2010 79844
Focus on 2012
Since 2012 had the highest counts of hate crimes, let’s look at the populations of the counties in 2012.
Clean the nypoplong12 variable, county, so that matches the counties12 variable by Cutting off the “, New York” portion of the county listing
nypoplong12 <- nypoplong |>filter(year ==2012) |>arrange(desc(population)) |>head(10)nypoplong12$county<-gsub(" , New York","",nypoplong12$county)nypoplong12
# A tibble: 10 × 3
county year population
<chr> <dbl> <dbl>
1 Kings 2012 2572282
2 Queens 2012 2278024
3 New York 2012 1625121
4 Suffolk 2012 1499382
5 Bronx 2012 1414774
6 Nassau 2012 1350748
7 Westchester 2012 961073
8 Erie 2012 920792
9 Monroe 2012 748947
10 Richmond 2012 470978
Not surprisingly, 4/5 of the counties with the highest populations also were listed in the counties with the highest number of hate crimes. Only the Bronx, which has the fifth highest population is not in the list with the highest number of total hate crimes over the period from 2010 to 2016.
Recall the total hate crime counts:
Kings 713 New York 459 Suffolk 360 Nassau 298 Queens 235
# A tibble: 41 × 5
# Groups: year [1]
year county sum population rate
<dbl> <chr> <dbl> <dbl> <dbl>
1 2012 Suffolk 83 1499382 5.54
2 2012 Kings 136 2572282 5.29
3 2012 New York 71 1625121 4.37
4 2012 Richmond 18 470978 3.82
5 2012 Nassau 48 1350748 3.55
6 2012 Erie 28 920792 3.04
7 2012 Queens 48 2278024 2.11
8 2012 Bronx 23 1414774 1.63
9 2012 Westchester 13 961073 1.35
10 2012 Monroe 5 748947 0.668
# ℹ 31 more rows
Notice that the highest rates of hate crimes in 2012 happened in:
dt <- datajoinrate[,c("county","rate")]dt
# A tibble: 41 × 2
county rate
<chr> <dbl>
1 Suffolk 5.54
2 Kings 5.29
3 New York 4.37
4 Richmond 3.82
5 Nassau 3.55
6 Erie 3.04
7 Queens 2.11
8 Bronx 1.63
9 Westchester 1.35
10 Monroe 0.668
# ℹ 31 more rows
But the highest populated counties were: Kings (Brooklyn), Queens, New York, Suffolk (Long Island), Bronx, and Nassau. They do not correspond directly, though they are similar, to the counties with highest rates of hate crimes.
I wonder what the data would look like if there was a universally accepted requirement for this type of data collection.
The Bronx appears to have much lower than expected incvictim_catents of hate crimes relative to its population density in comparison to other NY counties.
In Kings County, NY (which is home to Brooklyn; according to Wikipedia, it is New York’s most populous borough and the second most densly populated county in the US) in 2012, there was a spike in hate crimes against jews.
All of these findings are corroborated in Hate Crime in New York State 2012 Annual Report: https://www.criminaljustice.ny.gov/crimnet/ojsa/hate-crime-in-nys-2012-annual-report.pdf
Assignment:
Based on what we discussed in class, write an essay that incorporates the following: Positive and negative aspects of this dataset. List 2 different paths you would like to (hypothetically) study about this dataset. Describe 2 things you would do to follow up after seeing these results.
This dataset provides detailed information on hate crimes at the county level, enabling localized analysis of trends. Covering a span of seven years, the dataset allows for the identification of long-term trends and year-over-year changes. Additionally, this helps to understand the motivation and context behind the crime types. The data also offers a glimpse into evaluating the effectiveness of policies and interventions aimed at reducing hate crimes.
However, there are some limitations. There might be inconsistencies or gaps in the data due to underreporting or variations in data collection methods across counties. Without additional socioeconomic or demographic data, understanding the root causes of hate crimes can be challenging. Differences in how hate crimes are reported or classified in different counties can affect the accuracy and comparability of the data. The dataset ends in 2016, potentially missing out on more recent trends and changes in hate crime patterns, especially after the Black Lives Matter movement during the pandemic. If the data includes raw counts of hate crimes without further breakdowns, it limits the depth of analysis that can be conducted.
If data is aggregated at the county level without detailed incident-level information, it can obscure important patterns and nuances, such as urban versus rural differences within a county. Variations in the time frames for which data is available or collected might exist. For example, some counties might have more complete data for certain years than others. Additionally, providing data for seasonal variations could help analyze the causes of crimes due to seasons, which can be useful for developing policies for specific time periods. The lack of comparative data from other states or regions makes it difficult to contextualize whether trends in New York are unique or part of broader national patterns. Detailed datasets on hate crimes might risk the privacy of victims or could be used to further stigmatize certain communities if not handled and reported responsibly. Changes in political leadership or social climate over the years might affect the reporting and recording of hate crimes, introducing additional layers of complexity and potential bias in the data. Without proper context and understanding, the data could be misinterpreted, leading to incorrect conclusions about the prevalence or causes of hate crimes.
I would like to investigate the relationship between socioeconomic and demographic factors and the incidence of hate crimes in different counties. To achieve this, I will combine the hate crime dataset with socioeconomic and demographic data, considering factors such as income levels, unemployment rates, education levels, racial and ethnic composition, and population density. Additionally, we can analyze the patterns, underlying factors, and impact of anti-gender hate crimes in New York counties by filtering the dataset to focus on anti-gender hate crimes targeting individuals based on their gender or gender identity. We can categorize incidents based on the specific type of gender bias and investigate the relationship between anti-gender hate crimes and local cultural or political climates, including gender equality initiatives and laws. Furthermore, we can study the dynamics, causes, and effects of anti-religion hate crimes in New York counties by extracting data on hate crimes motivated by religious bias and analyzing temporal trends to identify any significant fluctuations over the years. Comparing hate crime rates against different religious groups will help identify which groups are most frequently targeted and whether major events such as attacks or significant religious events trigger increases in hate crimes.
Based on the findings, I would develop targeted policy recommendations for local and state governments. This could involve proposing new legislation, enhancing community policing, or increasing funding for hate crime prevention programs. I would also launch community outreach and education initiatives in counties with high rates of hate crimes, focusing on promoting tolerance, diversity, and understanding among different community groups. Additionally, I would provide support systems for victims, such as legal assistance, hotlines for reporting hate crimes, and counseling services. Developing cultural competency programs for public servants, including healthcare providers, teachers, and local government officials, would be crucial. Finally, providing economic support programs in communities with high hate crime rates can address underlying socioeconomic issues.
